Overview

Dataset statistics

Number of variables27
Number of observations20047
Missing cells0
Missing cells (%)0.0%
Duplicate rows1292
Duplicate rows (%)6.4%
Total size in memory4.5 MiB
Average record size in memory235.6 B

Variable types

Categorical8
Numeric19

Alerts

незачет has constant value ""Constant
Dataset has 1292 (6.4%) duplicate rowsDuplicates
удовлетворительно is highly overall correlated with отлично and 1 other fieldsHigh correlation
отлично is highly overall correlated with удовлетворительно and 1 other fieldsHigh correlation
зачет испр is highly overall correlated with незачет до испр and 1 other fieldsHigh correlation
незачет до испр is highly overall correlated with зачет испр and 1 other fieldsHigh correlation
Накоп зачет is highly overall correlated with Накоп хорошоHigh correlation
Накоп удовлетворительно is highly overall correlated with удовлетворительноHigh correlation
Накоп хорошо is highly overall correlated with Накоп зачетHigh correlation
Накоп отлично is highly overall correlated with отличноHigh correlation
Накоп зачет испр is highly overall correlated with Накоп незачет до испр and 1 other fieldsHigh correlation
Накоп удовлетворительно испр is highly overall correlated with Накоп зачет до испрHigh correlation
Накоп хорошо испр is highly overall correlated with Накоп удовлетворительно до испрHigh correlation
Накоп отлично испр is highly overall correlated with Накоп хорошо до испрHigh correlation
Накоп незачет до испр is highly overall correlated with Накоп зачет испр and 1 other fieldsHigh correlation
Накоп зачет до испр is highly overall correlated with Накоп удовлетворительно испрHigh correlation
Накоп удовлетворительно до испр is highly overall correlated with Накоп хорошо испрHigh correlation
Накоп хорошо до испр is highly overall correlated with Накоп отлично испрHigh correlation
удовлетворительно испр is highly overall correlated with зачет испр and 3 other fieldsHigh correlation
хорошо испр is highly overall correlated with удовлетворительно до испрHigh correlation
отлично испр is highly overall correlated with хорошо до испрHigh correlation
удовлетворительно до испр is highly overall correlated with хорошо испрHigh correlation
хорошо до испр is highly overall correlated with отлично испрHigh correlation
удовлетворительно испр is highly imbalanced (89.6%)Imbalance
хорошо испр is highly imbalanced (79.4%)Imbalance
отлично испр is highly imbalanced (72.2%)Imbalance
зачет до испр is highly imbalanced (73.5%)Imbalance
удовлетворительно до испр is highly imbalanced (81.8%)Imbalance
хорошо до испр is highly imbalanced (80.1%)Imbalance
зачет испр is highly skewed (γ1 = 34.53505256)Skewed
незачет до испр is highly skewed (γ1 = 30.97000687)Skewed
Накоп зачет испр is highly skewed (γ1 = 24.78864068)Skewed
Накоп незачет до испр is highly skewed (γ1 = 20.34159649)Skewed
зачет has 349 (1.7%) zerosZeros
удовлетворительно has 12140 (60.6%) zerosZeros
хорошо has 6853 (34.2%) zerosZeros
отлично has 9240 (46.1%) zerosZeros
зачет испр has 19987 (99.7%) zerosZeros
незачет до испр has 19876 (99.1%) zerosZeros
Накоп незачет has 19070 (95.1%) zerosZeros
Накоп удовлетворительно has 7897 (39.4%) zerosZeros
Накоп хорошо has 2641 (13.2%) zerosZeros
Накоп отлично has 4370 (21.8%) zerosZeros
Накоп зачет испр has 19761 (98.6%) zerosZeros
Накоп удовлетворительно испр has 17931 (89.4%) zerosZeros
Накоп хорошо испр has 15384 (76.7%) zerosZeros
Накоп отлично испр has 14252 (71.1%) zerosZeros
Накоп незачет до испр has 19347 (96.5%) zerosZeros
Накоп зачет до испр has 14651 (73.1%) zerosZeros
Накоп удовлетворительно до испр has 16926 (84.4%) zerosZeros
Накоп хорошо до испр has 16031 (80.0%) zerosZeros

Reproduction

Analysis started2023-10-05 09:24:37.472036
Analysis finished2023-10-05 09:25:58.151545
Duration1 minute and 20.68 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

незачет
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size541.2 KiB
0
20047 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20047
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20047
100.0%

Length

2023-10-05T16:25:58.376475image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T16:25:58.556269image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0 20047
100.0%

Most occurring characters

ValueCountFrequency (%)
0 20047
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20047
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20047
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 20047
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20047
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20047
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20047
100.0%

зачет
Real number (ℝ)

ZEROS 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9567516
Minimum0
Maximum11
Zeros349
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size541.2 KiB
2023-10-05T16:25:58.700076image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median4
Q35
95-th percentile6
Maximum11
Range11
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.6298973
Coefficient of variation (CV)0.41192812
Kurtosis0.27965138
Mean3.9567516
Median Absolute Deviation (MAD)1
Skewness-0.042078995
Sum79321
Variance2.6565651
MonotonicityNot monotonic
2023-10-05T16:25:58.841522image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
4 5726
28.6%
5 4219
21.0%
3 2917
14.6%
6 2471
12.3%
2 2397
12.0%
1 1206
 
6.0%
7 526
 
2.6%
0 349
 
1.7%
8 131
 
0.7%
10 50
 
0.2%
Other values (2) 55
 
0.3%
ValueCountFrequency (%)
0 349
 
1.7%
1 1206
 
6.0%
2 2397
12.0%
3 2917
14.6%
4 5726
28.6%
5 4219
21.0%
6 2471
12.3%
7 526
 
2.6%
8 131
 
0.7%
9 45
 
0.2%
ValueCountFrequency (%)
11 10
 
< 0.1%
10 50
 
0.2%
9 45
 
0.2%
8 131
 
0.7%
7 526
 
2.6%
6 2471
12.3%
5 4219
21.0%
4 5726
28.6%
3 2917
14.6%
2 2397
12.0%

удовлетворительно
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.75502569
Minimum0
Maximum8
Zeros12140
Zeros (%)60.6%
Negative0
Negative (%)0.0%
Memory size541.2 KiB
2023-10-05T16:25:58.993740image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1303795
Coefficient of variation (CV)1.4971404
Kurtosis1.5572831
Mean0.75502569
Median Absolute Deviation (MAD)0
Skewness1.4901255
Sum15136
Variance1.2777577
MonotonicityNot monotonic
2023-10-05T16:25:59.138298image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 12140
60.6%
1 3480
 
17.4%
2 2402
 
12.0%
3 1367
 
6.8%
4 561
 
2.8%
5 78
 
0.4%
6 17
 
0.1%
8 1
 
< 0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
0 12140
60.6%
1 3480
 
17.4%
2 2402
 
12.0%
3 1367
 
6.8%
4 561
 
2.8%
5 78
 
0.4%
6 17
 
0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
8 1
 
< 0.1%
7 1
 
< 0.1%
6 17
 
0.1%
5 78
 
0.4%
4 561
 
2.8%
3 1367
 
6.8%
2 2402
 
12.0%
1 3480
 
17.4%
0 12140
60.6%

хорошо
Real number (ℝ)

ZEROS 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.256597
Minimum0
Maximum11
Zeros6853
Zeros (%)34.2%
Negative0
Negative (%)0.0%
Memory size541.2 KiB
2023-10-05T16:25:59.301861image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile3
Maximum11
Range11
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2099323
Coefficient of variation (CV)0.96286424
Kurtosis1.0303577
Mean1.256597
Median Absolute Deviation (MAD)1
Skewness0.87758183
Sum25191
Variance1.4639362
MonotonicityNot monotonic
2023-10-05T16:25:59.445922image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 6853
34.2%
1 5598
27.9%
2 4326
21.6%
3 2335
 
11.6%
4 820
 
4.1%
5 80
 
0.4%
6 18
 
0.1%
9 6
 
< 0.1%
7 4
 
< 0.1%
8 3
 
< 0.1%
Other values (2) 4
 
< 0.1%
ValueCountFrequency (%)
0 6853
34.2%
1 5598
27.9%
2 4326
21.6%
3 2335
 
11.6%
4 820
 
4.1%
5 80
 
0.4%
6 18
 
0.1%
7 4
 
< 0.1%
8 3
 
< 0.1%
9 6
 
< 0.1%
ValueCountFrequency (%)
11 2
 
< 0.1%
10 2
 
< 0.1%
9 6
 
< 0.1%
8 3
 
< 0.1%
7 4
 
< 0.1%
6 18
 
0.1%
5 80
 
0.4%
4 820
 
4.1%
3 2335
11.6%
2 4326
21.6%

отлично
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1237592
Minimum0
Maximum13
Zeros9240
Zeros (%)46.1%
Negative0
Negative (%)0.0%
Memory size541.2 KiB
2023-10-05T16:25:59.602053image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile4
Maximum13
Range13
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3402486
Coefficient of variation (CV)1.1926475
Kurtosis2.2245339
Mean1.1237592
Median Absolute Deviation (MAD)1
Skewness1.2312156
Sum22528
Variance1.7962663
MonotonicityNot monotonic
2023-10-05T16:25:59.773278image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 9240
46.1%
1 4394
21.9%
2 2794
 
13.9%
3 2226
 
11.1%
4 1232
 
6.1%
5 114
 
0.6%
6 20
 
0.1%
7 8
 
< 0.1%
8 5
 
< 0.1%
9 4
 
< 0.1%
Other values (4) 10
 
< 0.1%
ValueCountFrequency (%)
0 9240
46.1%
1 4394
21.9%
2 2794
 
13.9%
3 2226
 
11.1%
4 1232
 
6.1%
5 114
 
0.6%
6 20
 
0.1%
7 8
 
< 0.1%
8 5
 
< 0.1%
9 4
 
< 0.1%
ValueCountFrequency (%)
13 3
 
< 0.1%
12 3
 
< 0.1%
11 3
 
< 0.1%
10 1
 
< 0.1%
9 4
 
< 0.1%
8 5
 
< 0.1%
7 8
 
< 0.1%
6 20
 
0.1%
5 114
 
0.6%
4 1232
6.1%

зачет испр
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0043398015
Minimum0
Maximum5
Zeros19987
Zeros (%)99.7%
Negative0
Negative (%)0.0%
Memory size541.2 KiB
2023-10-05T16:25:59.927650image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.099038185
Coefficient of variation (CV)22.820902
Kurtosis1462.4019
Mean0.0043398015
Median Absolute Deviation (MAD)0
Skewness34.535053
Sum87
Variance0.0098085622
MonotonicityNot monotonic
2023-10-05T16:26:00.092086image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 19987
99.7%
1 49
 
0.2%
2 4
 
< 0.1%
5 3
 
< 0.1%
4 3
 
< 0.1%
3 1
 
< 0.1%
ValueCountFrequency (%)
0 19987
99.7%
1 49
 
0.2%
2 4
 
< 0.1%
3 1
 
< 0.1%
4 3
 
< 0.1%
5 3
 
< 0.1%
ValueCountFrequency (%)
5 3
 
< 0.1%
4 3
 
< 0.1%
3 1
 
< 0.1%
2 4
 
< 0.1%
1 49
 
0.2%
0 19987
99.7%

удовлетворительно испр
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size541.2 KiB
0.0
19424 
1.0
 
592
2.0
 
30
3.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters60141
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 19424
96.9%
1.0 592
 
3.0%
2.0 30
 
0.1%
3.0 1
 
< 0.1%

Length

2023-10-05T16:26:00.256826image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T16:26:00.464788image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 19424
96.9%
1.0 592
 
3.0%
2.0 30
 
0.1%
3.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 39471
65.6%
. 20047
33.3%
1 592
 
1.0%
2 30
 
< 0.1%
3 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 40094
66.7%
Other Punctuation 20047
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 39471
98.4%
1 592
 
1.5%
2 30
 
0.1%
3 1
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 20047
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 60141
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 39471
65.6%
. 20047
33.3%
1 592
 
1.0%
2 30
 
< 0.1%
3 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 60141
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 39471
65.6%
. 20047
33.3%
1 592
 
1.0%
2 30
 
< 0.1%
3 1
 
< 0.1%

хорошо испр
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size541.2 KiB
0.0
18202 
1.0
 
1717
2.0
 
123
3.0
 
3
4.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters60141
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row1.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 18202
90.8%
1.0 1717
 
8.6%
2.0 123
 
0.6%
3.0 3
 
< 0.1%
4.0 2
 
< 0.1%

Length

2023-10-05T16:26:00.629836image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T16:26:00.821347image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 18202
90.8%
1.0 1717
 
8.6%
2.0 123
 
0.6%
3.0 3
 
< 0.1%
4.0 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 38249
63.6%
. 20047
33.3%
1 1717
 
2.9%
2 123
 
0.2%
3 3
 
< 0.1%
4 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 40094
66.7%
Other Punctuation 20047
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 38249
95.4%
1 1717
 
4.3%
2 123
 
0.3%
3 3
 
< 0.1%
4 2
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 20047
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 60141
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 38249
63.6%
. 20047
33.3%
1 1717
 
2.9%
2 123
 
0.2%
3 3
 
< 0.1%
4 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 60141
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 38249
63.6%
. 20047
33.3%
1 1717
 
2.9%
2 123
 
0.2%
3 3
 
< 0.1%
4 2
 
< 0.1%

отлично испр
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size541.2 KiB
0.0
17349 
1.0
2372 
2.0
 
310
3.0
 
14
4.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters60141
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 17349
86.5%
1.0 2372
 
11.8%
2.0 310
 
1.5%
3.0 14
 
0.1%
4.0 2
 
< 0.1%

Length

2023-10-05T16:26:00.981523image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T16:26:01.165454image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 17349
86.5%
1.0 2372
 
11.8%
2.0 310
 
1.5%
3.0 14
 
0.1%
4.0 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 37396
62.2%
. 20047
33.3%
1 2372
 
3.9%
2 310
 
0.5%
3 14
 
< 0.1%
4 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 40094
66.7%
Other Punctuation 20047
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 37396
93.3%
1 2372
 
5.9%
2 310
 
0.8%
3 14
 
< 0.1%
4 2
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 20047
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 60141
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 37396
62.2%
. 20047
33.3%
1 2372
 
3.9%
2 310
 
0.5%
3 14
 
< 0.1%
4 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 60141
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 37396
62.2%
. 20047
33.3%
1 2372
 
3.9%
2 310
 
0.5%
3 14
 
< 0.1%
4 2
 
< 0.1%

незачет до испр
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.011872101
Minimum0
Maximum8
Zeros19876
Zeros (%)99.1%
Negative0
Negative (%)0.0%
Memory size541.2 KiB
2023-10-05T16:26:01.329604image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum8
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.18161184
Coefficient of variation (CV)15.297364
Kurtosis1234.6943
Mean0.011872101
Median Absolute Deviation (MAD)0
Skewness30.970007
Sum238
Variance0.032982861
MonotonicityNot monotonic
2023-10-05T16:26:01.483428image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 19876
99.1%
1 150
 
0.7%
2 11
 
0.1%
8 6
 
< 0.1%
4 1
 
< 0.1%
6 1
 
< 0.1%
5 1
 
< 0.1%
3 1
 
< 0.1%
ValueCountFrequency (%)
0 19876
99.1%
1 150
 
0.7%
2 11
 
0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%
5 1
 
< 0.1%
6 1
 
< 0.1%
8 6
 
< 0.1%
ValueCountFrequency (%)
8 6
 
< 0.1%
6 1
 
< 0.1%
5 1
 
< 0.1%
4 1
 
< 0.1%
3 1
 
< 0.1%
2 11
 
0.1%
1 150
 
0.7%
0 19876
99.1%

зачет до испр
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size541.2 KiB
0.0
17980 
1.0
1851 
2.0
 
214
3.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters60141
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 17980
89.7%
1.0 1851
 
9.2%
2.0 214
 
1.1%
3.0 2
 
< 0.1%

Length

2023-10-05T16:26:01.672285image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T16:26:01.872487image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 17980
89.7%
1.0 1851
 
9.2%
2.0 214
 
1.1%
3.0 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 38027
63.2%
. 20047
33.3%
1 1851
 
3.1%
2 214
 
0.4%
3 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 40094
66.7%
Other Punctuation 20047
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 38027
94.8%
1 1851
 
4.6%
2 214
 
0.5%
3 2
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 20047
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 60141
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 38027
63.2%
. 20047
33.3%
1 1851
 
3.1%
2 214
 
0.4%
3 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 60141
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 38027
63.2%
. 20047
33.3%
1 1851
 
3.1%
2 214
 
0.4%
3 2
 
< 0.1%

удовлетворительно до испр
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size541.2 KiB
0.0
18782 
1.0
 
1171
2.0
 
93
3.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters60141
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 18782
93.7%
1.0 1171
 
5.8%
2.0 93
 
0.5%
3.0 1
 
< 0.1%

Length

2023-10-05T16:26:02.028448image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T16:26:02.215417image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 18782
93.7%
1.0 1171
 
5.8%
2.0 93
 
0.5%
3.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 38829
64.6%
. 20047
33.3%
1 1171
 
1.9%
2 93
 
0.2%
3 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 40094
66.7%
Other Punctuation 20047
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 38829
96.8%
1 1171
 
2.9%
2 93
 
0.2%
3 1
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 20047
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 60141
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 38829
64.6%
. 20047
33.3%
1 1171
 
1.9%
2 93
 
0.2%
3 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 60141
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 38829
64.6%
. 20047
33.3%
1 1171
 
1.9%
2 93
 
0.2%
3 1
 
< 0.1%

хорошо до испр
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size541.2 KiB
0.0
18339 
1.0
 
1544
2.0
 
157
3.0
 
5
4.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters60141
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 18339
91.5%
1.0 1544
 
7.7%
2.0 157
 
0.8%
3.0 5
 
< 0.1%
4.0 2
 
< 0.1%

Length

2023-10-05T16:26:02.367864image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T16:26:02.543313image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 18339
91.5%
1.0 1544
 
7.7%
2.0 157
 
0.8%
3.0 5
 
< 0.1%
4.0 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 38386
63.8%
. 20047
33.3%
1 1544
 
2.6%
2 157
 
0.3%
3 5
 
< 0.1%
4 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 40094
66.7%
Other Punctuation 20047
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 38386
95.7%
1 1544
 
3.9%
2 157
 
0.4%
3 5
 
< 0.1%
4 2
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 20047
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 60141
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 38386
63.8%
. 20047
33.3%
1 1544
 
2.6%
2 157
 
0.3%
3 5
 
< 0.1%
4 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 60141
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 38386
63.8%
. 20047
33.3%
1 1544
 
2.6%
2 157
 
0.3%
3 5
 
< 0.1%
4 2
 
< 0.1%

Накоп незачет
Real number (ℝ)

ZEROS 

Distinct27
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.298299
Minimum0
Maximum39
Zeros19070
Zeros (%)95.1%
Negative0
Negative (%)0.0%
Memory size541.2 KiB
2023-10-05T16:26:02.703384image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum39
Range39
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.7394681
Coefficient of variation (CV)5.8312906
Kurtosis93.623665
Mean0.298299
Median Absolute Deviation (MAD)0
Skewness8.5203338
Sum5980
Variance3.0257494
MonotonicityNot monotonic
2023-10-05T16:26:02.863172image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0 19070
95.1%
2 300
 
1.5%
3 111
 
0.6%
4 103
 
0.5%
5 69
 
0.3%
6 67
 
0.3%
8 57
 
0.3%
7 46
 
0.2%
10 31
 
0.2%
11 30
 
0.1%
Other values (17) 163
 
0.8%
ValueCountFrequency (%)
0 19070
95.1%
2 300
 
1.5%
3 111
 
0.6%
4 103
 
0.5%
5 69
 
0.3%
6 67
 
0.3%
7 46
 
0.2%
8 57
 
0.3%
9 24
 
0.1%
10 31
 
0.2%
ValueCountFrequency (%)
39 1
 
< 0.1%
35 1
 
< 0.1%
31 1
 
< 0.1%
28 3
 
< 0.1%
26 3
 
< 0.1%
24 3
 
< 0.1%
22 9
< 0.1%
21 9
< 0.1%
20 1
 
< 0.1%
19 1
 
< 0.1%

Накоп зачет
Real number (ℝ)

HIGH CORRELATION 

Distinct71
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.972914
Minimum0
Maximum72
Zeros15
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size541.2 KiB
2023-10-05T16:26:03.091131image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q18
median15
Q328
95-th percentile39
Maximum72
Range72
Interquartile range (IQR)20

Descriptive statistics

Standard deviation12.218235
Coefficient of variation (CV)0.6798138
Kurtosis-0.46034569
Mean17.972914
Median Absolute Deviation (MAD)9
Skewness0.66124942
Sum360303
Variance149.28526
MonotonicityNot monotonic
2023-10-05T16:26:03.290865image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 1906
 
9.5%
8 1232
 
6.1%
10 1138
 
5.7%
11 1116
 
5.6%
9 812
 
4.1%
5 758
 
3.8%
15 657
 
3.3%
29 534
 
2.7%
13 497
 
2.5%
21 467
 
2.3%
Other values (61) 10930
54.5%
ValueCountFrequency (%)
0 15
 
0.1%
1 56
 
0.3%
2 301
 
1.5%
3 452
 
2.3%
4 1906
9.5%
5 758
 
3.8%
6 462
 
2.3%
7 417
 
2.1%
8 1232
6.1%
9 812
4.1%
ValueCountFrequency (%)
72 1
 
< 0.1%
70 1
 
< 0.1%
69 2
 
< 0.1%
67 3
< 0.1%
66 3
< 0.1%
65 2
 
< 0.1%
64 4
< 0.1%
63 2
 
< 0.1%
62 5
< 0.1%
61 4
< 0.1%

Накоп удовлетворительно
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct59
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1453085
Minimum0
Maximum64
Zeros7897
Zeros (%)39.4%
Negative0
Negative (%)0.0%
Memory size541.2 KiB
2023-10-05T16:26:03.541441image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q36
95-th percentile17
Maximum64
Range64
Interquartile range (IQR)6

Descriptive statistics

Standard deviation6.1720312
Coefficient of variation (CV)1.4889196
Kurtosis9.2941074
Mean4.1453085
Median Absolute Deviation (MAD)1
Skewness2.4889858
Sum83101
Variance38.09397
MonotonicityNot monotonic
2023-10-05T16:26:03.741289image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7897
39.4%
1 2164
 
10.8%
2 1648
 
8.2%
3 1144
 
5.7%
4 982
 
4.9%
5 857
 
4.3%
6 671
 
3.3%
7 592
 
3.0%
8 540
 
2.7%
10 473
 
2.4%
Other values (49) 3079
 
15.4%
ValueCountFrequency (%)
0 7897
39.4%
1 2164
 
10.8%
2 1648
 
8.2%
3 1144
 
5.7%
4 982
 
4.9%
5 857
 
4.3%
6 671
 
3.3%
7 592
 
3.0%
8 540
 
2.7%
9 430
 
2.1%
ValueCountFrequency (%)
64 1
< 0.1%
62 1
< 0.1%
61 2
< 0.1%
59 1
< 0.1%
58 1
< 0.1%
56 1
< 0.1%
54 1
< 0.1%
53 1
< 0.1%
52 1
< 0.1%
50 2
< 0.1%

Накоп хорошо
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct36
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.0262383
Minimum0
Maximum38
Zeros2641
Zeros (%)13.2%
Negative0
Negative (%)0.0%
Memory size541.2 KiB
2023-10-05T16:26:03.936649image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q39
95-th percentile17
Maximum38
Range38
Interquartile range (IQR)7

Descriptive statistics

Standard deviation5.4194426
Coefficient of variation (CV)0.89930771
Kurtosis0.57257715
Mean6.0262383
Median Absolute Deviation (MAD)4
Skewness0.99467299
Sum120808
Variance29.370358
MonotonicityNot monotonic
2023-10-05T16:26:04.119130image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
0 2641
13.2%
1 2169
10.8%
2 2058
10.3%
3 1769
 
8.8%
4 1348
 
6.7%
5 1180
 
5.9%
6 1129
 
5.6%
7 970
 
4.8%
8 912
 
4.5%
9 909
 
4.5%
Other values (26) 4962
24.8%
ValueCountFrequency (%)
0 2641
13.2%
1 2169
10.8%
2 2058
10.3%
3 1769
8.8%
4 1348
6.7%
5 1180
5.9%
6 1129
5.6%
7 970
 
4.8%
8 912
 
4.5%
9 909
 
4.5%
ValueCountFrequency (%)
38 1
 
< 0.1%
36 1
 
< 0.1%
35 1
 
< 0.1%
32 2
 
< 0.1%
31 4
< 0.1%
30 3
 
< 0.1%
29 5
< 0.1%
28 7
< 0.1%
27 9
< 0.1%
26 8
< 0.1%

Накоп отлично
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct40
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2906669
Minimum0
Maximum42
Zeros4370
Zeros (%)21.8%
Negative0
Negative (%)0.0%
Memory size541.2 KiB
2023-10-05T16:26:04.341245image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q36
95-th percentile16
Maximum42
Range42
Interquartile range (IQR)5

Descriptive statistics

Standard deviation5.3171832
Coefficient of variation (CV)1.239244
Kurtosis4.9859886
Mean4.2906669
Median Absolute Deviation (MAD)2
Skewness2.0834415
Sum86015
Variance28.272438
MonotonicityNot monotonic
2023-10-05T16:26:04.515736image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
0 4370
21.8%
1 3139
15.7%
2 2740
13.7%
3 2068
10.3%
4 1317
 
6.6%
5 1009
 
5.0%
6 983
 
4.9%
7 683
 
3.4%
8 622
 
3.1%
9 524
 
2.6%
Other values (30) 2592
12.9%
ValueCountFrequency (%)
0 4370
21.8%
1 3139
15.7%
2 2740
13.7%
3 2068
10.3%
4 1317
 
6.6%
5 1009
 
5.0%
6 983
 
4.9%
7 683
 
3.4%
8 622
 
3.1%
9 524
 
2.6%
ValueCountFrequency (%)
42 1
 
< 0.1%
41 1
 
< 0.1%
40 1
 
< 0.1%
39 1
 
< 0.1%
35 1
 
< 0.1%
34 1
 
< 0.1%
33 4
 
< 0.1%
32 6
 
< 0.1%
31 8
< 0.1%
30 19
0.1%

Накоп зачет испр
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.019903227
Minimum0
Maximum12
Zeros19761
Zeros (%)98.6%
Negative0
Negative (%)0.0%
Memory size541.2 KiB
2023-10-05T16:26:04.667500image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum12
Range12
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.22413698
Coefficient of variation (CV)11.261338
Kurtosis925.94613
Mean0.019903227
Median Absolute Deviation (MAD)0
Skewness24.788641
Sum399
Variance0.050237385
MonotonicityNot monotonic
2023-10-05T16:26:04.839747image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 19761
98.6%
1 233
 
1.2%
2 36
 
0.2%
3 6
 
< 0.1%
5 5
 
< 0.1%
9 3
 
< 0.1%
6 2
 
< 0.1%
12 1
 
< 0.1%
ValueCountFrequency (%)
0 19761
98.6%
1 233
 
1.2%
2 36
 
0.2%
3 6
 
< 0.1%
5 5
 
< 0.1%
6 2
 
< 0.1%
9 3
 
< 0.1%
12 1
 
< 0.1%
ValueCountFrequency (%)
12 1
 
< 0.1%
9 3
 
< 0.1%
6 2
 
< 0.1%
5 5
 
< 0.1%
3 6
 
< 0.1%
2 36
 
0.2%
1 233
 
1.2%
0 19761
98.6%

Накоп удовлетворительно испр
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.14924926
Minimum0
Maximum7
Zeros17931
Zeros (%)89.4%
Negative0
Negative (%)0.0%
Memory size541.2 KiB
2023-10-05T16:26:05.028227image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.50693318
Coefficient of variation (CV)3.396554
Kurtosis29.527857
Mean0.14924926
Median Absolute Deviation (MAD)0
Skewness4.6717616
Sum2992
Variance0.25698125
MonotonicityNot monotonic
2023-10-05T16:26:05.184013image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 17931
89.4%
1 1538
 
7.7%
2 362
 
1.8%
3 163
 
0.8%
4 36
 
0.2%
5 11
 
0.1%
7 6
 
< 0.1%
ValueCountFrequency (%)
0 17931
89.4%
1 1538
 
7.7%
2 362
 
1.8%
3 163
 
0.8%
4 36
 
0.2%
5 11
 
0.1%
7 6
 
< 0.1%
ValueCountFrequency (%)
7 6
 
< 0.1%
5 11
 
0.1%
4 36
 
0.2%
3 163
 
0.8%
2 362
 
1.8%
1 1538
 
7.7%
0 17931
89.4%

Накоп хорошо испр
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.36080212
Minimum0
Maximum7
Zeros15384
Zeros (%)76.7%
Negative0
Negative (%)0.0%
Memory size541.2 KiB
2023-10-05T16:26:05.347311image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.78253274
Coefficient of variation (CV)2.1688696
Kurtosis8.938374
Mean0.36080212
Median Absolute Deviation (MAD)0
Skewness2.7449108
Sum7233
Variance0.61235749
MonotonicityNot monotonic
2023-10-05T16:26:05.491756image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 15384
76.7%
1 2977
 
14.9%
2 1086
 
5.4%
3 387
 
1.9%
4 158
 
0.8%
5 40
 
0.2%
6 14
 
0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
0 15384
76.7%
1 2977
 
14.9%
2 1086
 
5.4%
3 387
 
1.9%
4 158
 
0.8%
5 40
 
0.2%
6 14
 
0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
7 1
 
< 0.1%
6 14
 
0.1%
5 40
 
0.2%
4 158
 
0.8%
3 387
 
1.9%
2 1086
 
5.4%
1 2977
 
14.9%
0 15384
76.7%

Накоп отлично испр
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.47109293
Minimum0
Maximum8
Zeros14252
Zeros (%)71.1%
Negative0
Negative (%)0.0%
Memory size541.2 KiB
2023-10-05T16:26:05.676124image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.91538117
Coefficient of variation (CV)1.9431011
Kurtosis8.5137796
Mean0.47109293
Median Absolute Deviation (MAD)0
Skewness2.5965232
Sum9444
Variance0.8379227
MonotonicityNot monotonic
2023-10-05T16:26:05.841641image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 14252
71.1%
1 3576
 
17.8%
2 1322
 
6.6%
3 561
 
2.8%
4 206
 
1.0%
5 84
 
0.4%
6 29
 
0.1%
7 13
 
0.1%
8 4
 
< 0.1%
ValueCountFrequency (%)
0 14252
71.1%
1 3576
 
17.8%
2 1322
 
6.6%
3 561
 
2.8%
4 206
 
1.0%
5 84
 
0.4%
6 29
 
0.1%
7 13
 
0.1%
8 4
 
< 0.1%
ValueCountFrequency (%)
8 4
 
< 0.1%
7 13
 
0.1%
6 29
 
0.1%
5 84
 
0.4%
4 206
 
1.0%
3 561
 
2.8%
2 1322
 
6.6%
1 3576
 
17.8%
0 14252
71.1%

Накоп незачет до испр
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.057415075
Minimum0
Maximum22
Zeros19347
Zeros (%)96.5%
Negative0
Negative (%)0.0%
Memory size541.2 KiB
2023-10-05T16:26:06.103378image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum22
Range22
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.45614385
Coefficient of variation (CV)7.9446704
Kurtosis646.11667
Mean0.057415075
Median Absolute Deviation (MAD)0
Skewness20.341596
Sum1151
Variance0.20806721
MonotonicityNot monotonic
2023-10-05T16:26:06.373858image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 19347
96.5%
1 512
 
2.6%
2 113
 
0.6%
3 23
 
0.1%
5 20
 
0.1%
4 13
 
0.1%
6 8
 
< 0.1%
8 3
 
< 0.1%
16 3
 
< 0.1%
12 2
 
< 0.1%
Other values (2) 3
 
< 0.1%
ValueCountFrequency (%)
0 19347
96.5%
1 512
 
2.6%
2 113
 
0.6%
3 23
 
0.1%
4 13
 
0.1%
5 20
 
0.1%
6 8
 
< 0.1%
8 3
 
< 0.1%
12 2
 
< 0.1%
13 2
 
< 0.1%
ValueCountFrequency (%)
22 1
 
< 0.1%
16 3
 
< 0.1%
13 2
 
< 0.1%
12 2
 
< 0.1%
8 3
 
< 0.1%
6 8
 
< 0.1%
5 20
 
0.1%
4 13
 
0.1%
3 23
 
0.1%
2 113
0.6%

Накоп зачет до испр
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.40754228
Minimum0
Maximum6
Zeros14651
Zeros (%)73.1%
Negative0
Negative (%)0.0%
Memory size541.2 KiB
2023-10-05T16:26:06.548146image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.80766246
Coefficient of variation (CV)1.9817882
Kurtosis7.0547061
Mean0.40754228
Median Absolute Deviation (MAD)0
Skewness2.4800088
Sum8170
Variance0.65231865
MonotonicityNot monotonic
2023-10-05T16:26:06.722617image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 14651
73.1%
1 3608
 
18.0%
2 1091
 
5.4%
3 472
 
2.4%
4 166
 
0.8%
5 54
 
0.3%
6 5
 
< 0.1%
ValueCountFrequency (%)
0 14651
73.1%
1 3608
 
18.0%
2 1091
 
5.4%
3 472
 
2.4%
4 166
 
0.8%
5 54
 
0.3%
6 5
 
< 0.1%
ValueCountFrequency (%)
6 5
 
< 0.1%
5 54
 
0.3%
4 166
 
0.8%
3 472
 
2.4%
2 1091
 
5.4%
1 3608
 
18.0%
0 14651
73.1%

Накоп удовлетворительно до испр
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2490647
Minimum0
Maximum6
Zeros16926
Zeros (%)84.4%
Negative0
Negative (%)0.0%
Memory size541.2 KiB
2023-10-05T16:26:06.871260image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.7020949
Coefficient of variation (CV)2.8189258
Kurtosis18.073236
Mean0.2490647
Median Absolute Deviation (MAD)0
Skewness3.831616
Sum4993
Variance0.49293724
MonotonicityNot monotonic
2023-10-05T16:26:07.007478image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 16926
84.4%
1 1992
 
9.9%
2 697
 
3.5%
3 232
 
1.2%
4 118
 
0.6%
5 53
 
0.3%
6 29
 
0.1%
ValueCountFrequency (%)
0 16926
84.4%
1 1992
 
9.9%
2 697
 
3.5%
3 232
 
1.2%
4 118
 
0.6%
5 53
 
0.3%
6 29
 
0.1%
ValueCountFrequency (%)
6 29
 
0.1%
5 53
 
0.3%
4 118
 
0.6%
3 232
 
1.2%
2 697
 
3.5%
1 1992
 
9.9%
0 16926
84.4%

Накоп хорошо до испр
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.28702549
Minimum0
Maximum7
Zeros16031
Zeros (%)80.0%
Negative0
Negative (%)0.0%
Memory size541.2 KiB
2023-10-05T16:26:07.148415image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.68209434
Coefficient of variation (CV)2.3764243
Kurtosis14.679355
Mean0.28702549
Median Absolute Deviation (MAD)0
Skewness3.2802298
Sum5754
Variance0.46525269
MonotonicityNot monotonic
2023-10-05T16:26:07.280240image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 16031
80.0%
1 2847
 
14.2%
2 801
 
4.0%
3 236
 
1.2%
4 92
 
0.5%
5 17
 
0.1%
6 17
 
0.1%
7 6
 
< 0.1%
ValueCountFrequency (%)
0 16031
80.0%
1 2847
 
14.2%
2 801
 
4.0%
3 236
 
1.2%
4 92
 
0.5%
5 17
 
0.1%
6 17
 
0.1%
7 6
 
< 0.1%
ValueCountFrequency (%)
7 6
 
< 0.1%
6 17
 
0.1%
5 17
 
0.1%
4 92
 
0.5%
3 236
 
1.2%
2 801
 
4.0%
1 2847
 
14.2%
0 16031
80.0%

отчислен
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size541.2 KiB
0
16805 
1
3242 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20047
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 16805
83.8%
1 3242
 
16.2%

Length

2023-10-05T16:26:07.449664image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-05T16:26:07.605052image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0 16805
83.8%
1 3242
 
16.2%

Most occurring characters

ValueCountFrequency (%)
0 16805
83.8%
1 3242
 
16.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20047
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16805
83.8%
1 3242
 
16.2%

Most occurring scripts

ValueCountFrequency (%)
Common 20047
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16805
83.8%
1 3242
 
16.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20047
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16805
83.8%
1 3242
 
16.2%

Interactions

2023-10-05T16:25:52.950509image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:24:41.803708image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:24:45.113410image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:24:48.633443image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:24:53.283090image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:24:57.443989image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:01.829724image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:06.998889image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:11.078991image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:14.783411image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:18.179404image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:21.457692image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:25.018080image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:29.121480image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:32.641714image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:35.936313image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:39.476902image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:43.109126image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:47.502578image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:53.126326image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:24:42.025160image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:24:45.289312image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:24:48.805171image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:24:53.467401image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:24:57.676259image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:01.996511image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:07.195397image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:11.328876image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:14.943586image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:18.342163image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:21.617685image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:25.187624image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:29.283988image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:32.819441image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:36.100391image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:39.637050image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:43.265624image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:47.887683image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:53.322378image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:24:42.219333image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:24:45.498659image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:24:49.007865image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:24:53.722762image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:24:57.944172image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:02.704486image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:07.366729image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:11.517064image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:15.137015image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:18.515460image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:21.793741image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:25.361993image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:29.455728image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:32.995424image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:36.288542image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:39.805650image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:43.441813image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:48.332844image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:53.500065image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:24:42.386908image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:24:45.677519image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:24:49.216433image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:24:53.988281image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:24:58.252364image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:03.045463image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:07.546557image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:11.679255image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:15.321068image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:18.686682image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:21.948421image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:25.521780image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:29.625744image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:33.155232image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:36.459312image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:39.944421image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:43.590979image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:48.778986image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:53.687808image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:24:42.549396image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:24:45.870124image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:24:49.452293image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:24:54.173859image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:24:58.525148image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:03.304407image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:07.751123image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
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2023-10-05T16:25:55.723373image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:24:44.403422image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:24:47.893374image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:24:51.570956image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:24:56.525087image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:00.955221image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:06.182375image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:10.205887image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:14.061682image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:17.449776image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:20.805477image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:24.383258image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:28.442220image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:31.863698image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:35.212293image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:38.697575image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:42.324609image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:45.867031image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:51.532974image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:55.927479image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:24:44.579308image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:24:48.073406image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:24:52.014786image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:24:56.769673image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:01.167664image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:06.420164image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:10.464571image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:14.220959image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:17.605794image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:20.953753image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:24.532314image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:28.601132image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:32.111209image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:35.383271image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:38.901324image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:42.498491image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:46.136133image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:51.690735image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:56.099887image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:24:44.749856image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:24:48.247229image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:24:52.598900image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:24:57.029330image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:01.441277image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:06.658722image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:10.728509image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:14.415223image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:17.791445image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:21.117368image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:24.693551image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:28.765252image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:32.286795image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:35.547258image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:39.095473image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:42.728652image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:46.436544image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:52.468813image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:56.277289image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:24:44.935733image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:24:48.435375image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:24:52.913382image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:24:57.211622image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:01.647034image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:06.823651image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:10.907251image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:14.599624image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:17.985455image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:21.285791image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:24.857906image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:28.945084image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:32.453120image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:35.756395image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:39.305598image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:42.941420image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:47.040211image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-10-05T16:25:52.755471image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Correlations

2023-10-05T16:26:07.764869image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
зачетудовлетворительнохорошоотличнозачет испрнезачет до испрНакоп незачетНакоп зачетНакоп удовлетворительноНакоп хорошоНакоп отличноНакоп зачет испрНакоп удовлетворительно испрНакоп хорошо испрНакоп отлично испрНакоп незачет до испрНакоп зачет до испрНакоп удовлетворительно до испрНакоп хорошо до испрудовлетворительно испрхорошо испротлично испрзачет до испрудовлетворительно до испрхорошо до испротчислен
зачет1.0000.0010.1940.145-0.043-0.036-0.0930.173-0.0350.1150.074-0.080-0.083-0.0350.008-0.0880.008-0.0480.0010.0720.0510.0750.1220.0380.0640.111
удовлетворительно0.0011.000-0.099-0.510-0.008-0.0050.0980.0430.6680.020-0.4650.0430.1810.084-0.2040.046-0.0150.150-0.1610.0770.0550.0840.0180.0810.0650.242
хорошо0.194-0.0991.000-0.289-0.019-0.030-0.0290.0290.0530.438-0.186-0.034-0.0060.038-0.039-0.0410.000-0.0150.0060.0000.0140.0450.0400.0510.0330.014
отлично0.145-0.510-0.2891.0000.008-0.010-0.1180.008-0.495-0.1530.670-0.038-0.181-0.1640.135-0.0590.024-0.2160.0510.0460.0590.0470.1020.0870.0480.203
зачет испр-0.043-0.008-0.0190.0081.0000.5910.017-0.044-0.019-0.035-0.0080.4560.0440.0130.0040.291-0.017-0.014-0.0030.5950.4110.0000.0000.0000.0000.044
незачет до испр-0.036-0.005-0.030-0.0100.5911.0000.050-0.045-0.010-0.046-0.0230.2910.1020.0260.0190.488-0.029-0.013-0.0070.6110.4090.0250.0000.0000.0000.062
Накоп незачет-0.0930.098-0.029-0.1180.0170.0501.0000.1700.2370.131-0.0390.1800.1690.1210.0120.2290.0880.1080.0010.0310.0210.0000.0170.0220.0000.388
Накоп зачет0.1730.0430.0290.008-0.044-0.0450.1701.0000.4760.7500.4640.0350.2670.4540.4160.0800.4830.3810.3210.0770.1020.0830.1630.1100.0620.175
Накоп удовлетворительно-0.0350.6680.053-0.495-0.019-0.0100.2370.4761.0000.417-0.3030.0800.3740.366-0.0100.1060.2380.3770.0150.0950.0700.0340.0510.0850.0240.203
Накоп хорошо0.1150.0200.438-0.153-0.035-0.0460.1310.7500.4171.0000.2340.0190.2430.4300.3030.0500.3960.3210.2760.0540.0910.0510.1050.0800.0420.092
Накоп отлично0.074-0.465-0.1860.670-0.008-0.023-0.0390.464-0.3030.2341.000-0.017-0.0290.0990.404-0.0110.301-0.0050.2810.0250.0250.0860.0770.0340.0450.178
Накоп зачет испр-0.0800.043-0.034-0.0380.4560.2910.1800.0350.0800.019-0.0171.0000.1430.0840.0020.6380.0340.059-0.0070.5860.4090.0000.0000.0000.0000.077
Накоп удовлетворительно испр-0.0830.181-0.006-0.1810.0440.1020.1690.2670.3740.243-0.0290.1431.0000.2060.0100.2750.5160.209-0.0070.4050.0640.0090.1700.0720.0000.100
Накоп хорошо испр-0.0350.0840.038-0.1640.0130.0260.1210.4540.3660.4300.0990.0840.2061.0000.2240.1500.4780.7370.1940.0260.3410.0370.1350.2800.0270.030
Накоп отлично испр0.008-0.204-0.0390.1350.0040.0190.0120.416-0.0100.3030.4040.0020.0100.2241.0000.0730.3890.2250.8110.0260.0250.3720.1270.0660.2980.136
Накоп незачет до испр-0.0880.046-0.041-0.0590.2910.4880.2290.0800.1060.050-0.0110.6380.2750.1500.0731.0000.0190.1190.0340.5940.4130.0000.0000.0000.0000.090
Накоп зачет до испр0.008-0.0150.0000.024-0.017-0.0290.0880.4830.2380.3960.3010.0340.5160.4780.3890.0191.0000.1700.1330.1770.1480.1350.3810.0620.0240.082
Накоп удовлетворительно до испр-0.0480.150-0.015-0.216-0.014-0.0130.1080.3810.3770.321-0.0050.0590.2090.7370.2250.1190.1701.0000.1610.0130.2280.0360.0220.3760.0120.025
Накоп хорошо до испр0.001-0.1610.0060.051-0.003-0.0070.0010.3210.0150.2760.281-0.007-0.0070.1940.8110.0340.1330.1611.0000.0230.0100.3410.0120.0390.4100.119
удовлетворительно испр0.0720.0770.0000.0460.5950.6110.0310.0770.0950.0540.0250.5860.4050.0260.0260.5940.1770.0130.0231.0000.0000.0280.3240.0000.0230.040
хорошо испр0.0510.0550.0140.0590.4110.4090.0210.1020.0700.0910.0250.4090.0640.3410.0250.4130.1480.2280.0100.0001.0000.0160.3180.6400.0100.026
отлично испр0.0750.0840.0450.0470.0000.0250.0000.0830.0340.0510.0860.0000.0090.0370.3720.0000.1350.0360.3410.0280.0161.0000.2630.0530.7770.093
зачет до испр0.1220.0180.0400.1020.0000.0000.0170.1630.0510.1050.0770.0000.1700.1350.1270.0000.3810.0220.0120.3240.3180.2631.0000.0190.0290.035
удовлетворительно до испр0.0380.0810.0510.0870.0000.0000.0220.1100.0850.0800.0340.0000.0720.2800.0660.0000.0620.3760.0390.0000.6400.0530.0191.0000.0000.017
хорошо до испр0.0640.0650.0330.0480.0000.0000.0000.0620.0240.0420.0450.0000.0000.0270.2980.0000.0240.0120.4100.0230.0100.7770.0290.0001.0000.073
отчислен0.1110.2420.0140.2030.0440.0620.3880.1750.2030.0920.1780.0770.1000.0300.1360.0900.0820.0250.1190.0400.0260.0930.0350.0170.0731.000

Missing values

2023-10-05T16:25:56.604881image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
A simple visualization of nullity by column.
2023-10-05T16:25:57.454663image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Оценканезачетзачетудовлетворительнохорошоотличнозачет испрудовлетворительно испрхорошо испротлично испрнезачет до испрзачет до испрудовлетворительно до испрхорошо до испрНакоп незачетНакоп зачетНакоп удовлетворительноНакоп хорошоНакоп отличноНакоп зачет испрНакоп удовлетворительно испрНакоп хорошо испрНакоп отлично испрНакоп незачет до испрНакоп зачет до испрНакоп удовлетворительно до испрНакоп хорошо до испротчислен
СтудентГруппаСеместрУчебный годСпециальностьФорма обученияКвалификацияСтатус
2B2F397249583175764B6A74722B373239642F7053413D3D7569304E30773043574C4F677453795A4C74305551673D3D12015/201638.03.01 ЭкономикаЗаочнаяБакалаврОтчисленный06.00.01.01.00.00.00.00.00.00.00.00.00.06.00.01.01.00.00.00.00.00.00.00.00.00
22015/201638.03.01 ЭкономикаЗаочнаяБакалаврОтчисленный05.01.00.02.00.00.01.00.00.00.01.00.00.011.01.01.03.00.00.01.00.00.00.01.00.00
32016/201738.03.01 ЭкономикаЗаочнаяБакалаврОтчисленный02.00.02.01.00.00.01.00.00.00.01.00.00.013.01.03.04.00.00.02.00.00.00.02.00.00
42016/201738.03.01 ЭкономикаЗаочнаяБакалаврОтчисленный04.00.00.02.00.00.00.01.00.00.01.00.00.017.01.03.06.00.00.02.01.00.00.03.00.00
52017/201838.03.01 ЭкономикаЗаочнаяБакалаврОтчисленный05.00.02.00.00.00.01.00.00.00.01.00.00.022.01.05.06.00.00.03.01.00.00.04.00.00
62017/201838.03.01 ЭкономикаЗаочнаяБакалаврОтчисленный04.00.02.00.00.00.01.00.00.00.01.00.00.026.01.07.06.00.00.04.01.00.00.05.00.00
72018/201938.03.01 ЭкономикаЗаочнаяБакалаврОтчисленный03.00.03.00.00.00.01.00.00.00.01.00.00.029.01.010.06.00.00.05.01.00.00.06.00.00
82018/201938.03.01 ЭкономикаЗаочнаяБакалаврОтчисленный05.00.01.01.00.00.00.01.00.00.00.01.00.034.01.011.07.00.00.05.02.00.00.06.01.00
92019/202038.03.01 ЭкономикаЗаочнаяБакалаврОтчисленный03.00.02.00.00.00.00.00.00.00.00.00.00.037.01.013.07.00.00.05.02.00.00.06.01.00
102019/202038.03.01 ЭкономикаЗаочнаяБакалаврОтчисленный01.00.00.01.00.00.00.01.00.00.00.01.00.038.01.013.08.00.00.05.03.00.00.06.02.00
Оценканезачетзачетудовлетворительнохорошоотличнозачет испрудовлетворительно испрхорошо испротлично испрнезачет до испрзачет до испрудовлетворительно до испрхорошо до испрНакоп незачетНакоп зачетНакоп удовлетворительноНакоп хорошоНакоп отличноНакоп зачет испрНакоп удовлетворительно испрНакоп хорошо испрНакоп отлично испрНакоп незачет до испрНакоп зачет до испрНакоп удовлетворительно до испрНакоп хорошо до испротчислен
СтудентГруппаСеместрУчебный годСпециальностьФорма обученияКвалификацияСтатус
7A7A634B583179437670457A6569626A356B65626D513D3D42706B45756251354A694E6A737452454275456E62413D3D22013/201420.03.01 Техносферная безопасностьЗаочнаяБакалаврОтчисленный05.01.03.00.00.00.00.00.00.00.00.00.00.012.03.010.00.00.00.00.00.00.00.00.00.01
2015/201620.03.01 Техносферная безопасностьЗаочнаяБакалаврОтчисленный05.02.01.00.00.00.00.00.00.00.00.00.00.017.05.011.00.00.00.00.00.00.00.00.00.01
32016/201720.03.01 Техносферная безопасностьЗаочнаяБакалаврОтчисленный03.02.01.00.00.00.00.00.00.00.00.00.08.025.08.014.00.00.00.00.00.00.00.00.00.01
42016/201720.03.01 Техносферная безопасностьЗаочнаяБакалаврОтчисленный03.01.03.00.00.00.00.00.00.00.00.00.08.028.09.017.00.00.00.00.00.00.00.00.00.01
52017/201820.03.01 Техносферная безопасностьЗаочнаяБакалаврОтчисленный06.00.01.01.00.00.00.00.00.00.00.00.08.034.09.018.01.00.00.00.00.00.00.00.00.01
62017/201820.03.01 Техносферная безопасностьЗаочнаяБакалаврОтчисленный06.00.02.00.00.00.00.00.00.00.00.00.08.040.09.020.01.00.00.00.00.00.00.00.00.01
72018/201920.03.01 Техносферная безопасностьЗаочнаяБакалаврОтчисленный04.00.02.01.00.00.00.00.00.00.00.00.08.044.09.022.02.00.00.00.00.00.00.00.00.01
82018/201920.03.01 Техносферная безопасностьЗаочнаяБакалаврОтчисленный05.00.01.01.00.00.00.01.00.00.00.01.08.049.09.023.03.00.00.00.01.00.00.00.01.01
92019/202020.03.01 Техносферная безопасностьЗаочнаяБакалаврОтчисленный02.01.02.00.00.00.00.01.00.00.00.01.08.051.010.025.03.00.00.00.02.00.00.00.02.01
102019/202020.03.01 Техносферная безопасностьЗаочнаяБакалаврОтчисленный01.00.02.00.00.00.00.00.00.00.00.00.08.052.010.027.03.00.00.00.02.00.00.00.02.01

Duplicate rows

Most frequently occurring

незачетзачетудовлетворительнохорошоотличнозачет испрудовлетворительно испрхорошо испротлично испрнезачет до испрзачет до испрудовлетворительно до испрхорошо до испрНакоп незачетНакоп зачетНакоп удовлетворительноНакоп хорошоНакоп отличноНакоп зачет испрНакоп удовлетворительно испрНакоп хорошо испрНакоп отлично испрНакоп незачет до испрНакоп зачет до испрНакоп удовлетворительно до испрНакоп хорошо до испротчислен# duplicates
41104.00.00.03.00.00.00.00.00.00.00.00.00.04.00.00.03.00.00.00.00.00.00.00.00.00235
7302.00.00.00.00.00.00.00.00.00.00.00.00.02.00.00.00.00.00.00.00.00.00.00.00.00169
42104.00.00.03.00.00.00.00.00.00.00.00.00.08.00.00.06.00.00.00.00.00.00.00.00.00128
49404.00.01.02.00.00.00.00.00.00.00.00.00.04.00.01.02.00.00.00.00.00.00.00.00.00125
9402.00.00.02.00.00.00.00.00.00.00.00.00.04.00.00.02.00.00.00.00.00.00.00.00.0095
39204.00.00.00.00.00.00.00.00.00.00.00.00.08.00.00.02.00.00.00.00.00.00.00.00.0092
53504.00.02.01.00.00.00.00.00.00.00.00.00.04.00.02.01.00.00.00.00.00.00.00.00.0091
9002.00.00.01.00.00.00.00.00.00.00.00.00.010.00.00.03.00.00.00.00.00.00.00.00.0080
58404.00.03.01.00.00.00.00.00.00.00.00.00.04.00.03.01.00.00.00.00.00.00.00.00.0073
65804.01.03.00.00.00.00.00.00.00.00.00.00.04.01.03.00.00.00.00.00.00.00.00.00.0062